Smart Helmet: Thresh-Learner–Online Machine Learning on Data Streams
Dhruv Garg1, Aditya Chitlangia2, Vetrivelan P3
1Dhruv Garg, Department of Computer Science, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
2Aditya Chitlangia, Department of Computer Science, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
3Vetrivelan P, Department of Electronics and Communications, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 466-473 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10851291S319/19©BEIESP | DOI: 10.35940/ijeat.A1085.1291S319
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Abstract: with an enormous generation and availability of time series data and streaming data, there is an increasing need for an automatic analyzing architecture to get fast interpretations and results. One of the significant potentiality of streaming analytics is to train and model each stream with unsupervised Machine Learning (ML) algorithms to detect anomalous behaviors, fuzzy patterns, and accidents in real-time. If executed reliably, each anomaly detection can be highly valuable for the application. In this paper, we propose a dynamic threshold setting system denoted as Thresh-Learner, mainly for the Internet of Things (IoT) applications that require anomaly detection. The proposed model enables a wide range of real-life applications where there is a necessity to set up a dynamic threshold over the streaming data to avoid anomalies, accidents or sending alerts to distant monitoring stations. We took the major problem of anomalies and accidents in coal mines due to coal fires and explosions. This results in loss of life due to the lack of automated alarming systems. We propose Thresh-Learner, a general purpose implementation for setting dynamic thresholds. We illustrate it through the Smart Helmet for coal mine workers which seamlessly integrates monitoring, analyzing and dynamic thresholds using IoT and analysis on the cloud.
Keywords: Anomaly Detection, Internet of Things, Online Machine Learning, Stream Processing.
Scope of the Article: Machine Learning